Synote Discussion. Extending Synote to support threaded discussions synchronised with recorded videos

Synote Discussion has been developed as an accessible cross device and cross browser HTML5 web-based collaborative replay, annotation and discussion extension of the award winning open source Synote which has since 2008 made web-based recordings easier to access, search, manage, and exploit for learners, teachers and others. While Synote enables users to create comments in ‘Synmarks’ synchronized with any point in a recording it does not support users to comment on these Synmarks in a discussion thread. Synote Discussion supports commenting on Synmarks stored as discussions in its own database and published as Linked data so they are available for Synote or other systems to use. This paper explains the requirements and design of Synote Discussion, presents the results of a usability study and summarises conclusions and future planned wor

Unobstrusive human activity recognition using smartphones and Hidden Markov Models

Accelerometer data is sufficient to compute human activity recognition, even with only a single accelerometer in use. Such data can be used for many pervasive computing applications, user activity being interpreted as real-time contextual information. This paper investigates activity recognition on smartphones, as they are a suitable platform for the implementation of context-aware pervasive systems. Many machine learning algorithms are suitable for this purpose, but Hidden Markov Models (HMMs) are particularly appropriate for their ability to exploit the sequential and temporal nature of data. This paper evaluates HMMs in unobstrusive activity recognition with the added restrictions resulting from the use of the smartphone platform

Unobstrusive human activity recognition using smartphones and Hidden Markov Models

Accelerometer data is sufficient to compute human activity recognition, even with only a single accelerometer in use. Such data can be used for many pervasive computing applications, user activity being interpreted as real-time contextual information. This paper investigates activity recognition on smartphones, as they are a suitable platform for the implementation of context-aware pervasive systems. Many machine learning algorithms are suitable for this purpose, but Hidden Markov Models (HMMs) are particularly appropriate for their ability to exploit the sequential and temporal nature of data. This paper evaluates HMMs in unobstrusive activity recognition with the added restrictions resulting from the use of the smartphone platform